Factor Features: Not Your “90s Quant”

Vincent Costa

Vincent Costa, CFA

Head of Global Quantitative Equities

Sanne de Boer

Sanne de Boer, PhD, CFA

Director of Quantitative Equity Research

Julia Casals, CFA

Equity Portfolio Analyst

The quant space has evolved dramatically—understanding what’s coming next requires a look back at where we came from.

Quantitative Investing: Fama and French’s Excellent Adventure

Quantitative investing is generally defined as investment processes that use scientific methods and follow a repeatable, rules-based approach. Although it has roots dating back to the 1980s, it did not grow in popularity until the 1990s, when Eugene Fama and Kenneth French’s published “Common Risk Factors in the Returns of Stocks and Bonds”. In this now famous article, these hitherto defenders of market efficiency acknowledged that certain sub-segments of stocks (e.g. size and value) carry their own risk premiums above that of the broad market. This concept sparked a broad range of quant strategies that, having survived the tech crunch of the late 90s, grew in popularity during the start of the 21st century. The category’s first major obstacle came in 2007 when many quant funds suffered massive losses over a few days mostly due to the unwinding of crowded trades – a period known as the “Quant Quake of ‘07”. After further fallout from the Global Financial Crisis in 2008, quant quickly fell out of favor, becoming an unspoken, dirty word among equity investors for the next five years.

The Comeback: Fresh Ideas Bring Quant Back to Life

As the information and technological capabilities supporting the strategies greatly expanded and improved, the approach became impossible to discount or ignore. Indeed, despite the setbacks, a new category of investing known as “Smart-Beta” emerged in 2013, providing investors with access to low-cost, transparent, systematic means of capturing a collection of generic factor-return premia. Between the academically-based Smart Beta strategies and proprietary, more sophisticated multi-factor models, investors now had a broad range of quantitative strategies to choose from. As a result, with quants regaining their credibility and assets under management growing (Figure 1), competition increased, forcing quantitative practitioners to accelerate their hunt for unique factors to differentiate themselves.

Figure 1. Quant-Managed Active Equity and Smart Beta Products
U.S. Equity Assets Under Management as a Share of U.S. Market Capitalization 2000 Through Q2 2019

Source: Strategic Insight Simfund, eVestment Alliance, FactSet Research Systems, Empirical Research Partners Analysis. Includes quantitative hedge funds, quantitatively-managed active equity mutual funds, and quantitatively-managed institutional active equity products. Global products that invest in U.S. equities are included and their U.S. equity assets are assumed to be proportional to the weight of the U.S. in the global equity market. Mutual funds and institutional products include U.S.-domiciled assets only. Hedge fund assets include stocks bought on leverage. Style and smart beta includes institutional products and ETFs.

Friends: The One Where Quant meets Fundamental

Prior to the Quant Quake, fundamental and quantitative managers existed alongside one another, but rarely did they interact. Quant and fundamental were considered two separate religions and it largely remained that way for some time. More recently, as the appetite for quantitative investing has grown, a relatively new category emerged; one that places importance on combining fundamental analysis with the quantitative ability commonly known as quantamental. While Voya’s quantitative process falls into this new category, we have always recognized the power of incorporating fundamental insights into quantitative processes. For almost two decades, fundamental analysis has been embedded in the DNA of our quantitative models. Since the inception of our quant investment platform, our models have been highly influenced by fundamental inputs and our strategy is an extension of human judgement. One could surmise that we were “quantamental” before the portmanteau went mainstream, and our experiences have only grown over time as data and modeling techniques evolved.

Indeed, one of our key competitive advantages overall comes from the insights gleaned from our quant practitioners and fundamental analysts, who each have sector specialties and market insights that academic research tends to overlook. Moreover, the cognitive diversity derived from having both a fundamental and quantitative view benefits both sides in periods of market distress:

  • Fundamental Analysts can ask the quants “what are the models telling us?” and avoid acting on emotion;
  • The Quants can ask the analysts for qualitative insights and avoid taking a blind eye to events that backward-looking models may not yet capture.


With the advent of ever more powerful data mining techniques comes the increasing likelihood of over-fitting spurious investment signals to past return patterns. However, throughout this series our goal is to demonstrate that taking advantage of newly available data and the latest machine learning techniques do not have to be a precursor for black-box models—instead this approach can provide opportunities to capture fundamental intuition in more nuanced ways. Even in the era of ever greater computational power, the quantamental approach that marries fundamental expert systems with robotic intelligence may prove a winning combination.

 

Voya Investment Management has prepared this commentary for informational purposes. Nothing contained herein should be construed as (i) an offer to sell or solicitation of an offer to buy any security or (ii) a recommendation as to the advisability of investing in, purchasing or selling any security. Any opinions expressed herein reflect our judgment and are subject to change. Certain of the statements contained herein are statements of future expectations and other forward-looking statements that are based on management’s current views and assumptions, and involve known and unknown risks and uncertainties that could cause actual results, performance or events to differ materially from those expressed or implied in such statements. Actual results, performance or events may differ materially from those in such statements due to, without limitation, (1) general economic conditions, (2) performance of financial markets, (3) interest-rate levels, (4) increasing levels of loan defaults, (5) changes in laws and regulations, and (6) changes in the policies of governments and/or regulatory authorities.

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